The Mechanisms app Development of a new learning tool for active learning


Next steps: Pattern recognition from data



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the-mechanisms-app-development-of-a-new-learning-tool-for-active-learning-in-organic-chemistry

Next steps: Pattern recognition from data
 
Organic chemistry textbooks and lectures cover the general mechanism for 
many standard reaction types. On their own, students work through examples 
and check their proposed mechanism against these examples. However, unless a 
student comes to office hours, or the instructor circles through an active learning 
class, the instructor does not have the chance to see what students’ initial 
mechanistic instincts are. What is unknown is how much thought do students put 
into understanding why their proposed mechanism is incorrect. What if 
instructors could see what the common initial mistakes are among students? 
Could they provide more clarity to students in lecture or extra feedback on an 
answer key? Would this information lead to a major advancement in identifying 
and breaking down barriers to understanding mechanisms? These are 
compelling questions, but there are limited assessment tools that give instructors 
acces to realtime student thinking. Perhaps one of the most exciting features of 
Mechanisms for instructors is the app’s ability to record ALL the moves 
students attempt. To date, the database of Mechanisms has collected 
approximately 250,000 user sessions from the app, with all moves coded as to 
sequence and type, such as nucleophilic attack or deprotonation.
Initially, Alchemie’s chemistry content experts were surprised by some of the 
emerging common errors in the student data. From the lens of a novice though, 


The Mechanisms app - Development of a new learning tool for active learning 
in organic chemistry v13 COMPRESSED 
Printed 
2/28/2019 
20 
these moves begin to make sense and reveal where more guidance and 
explanation is required. For example, reviewing data from Addition Puzzle 2, 
hydration of 2-butene, exposed the belief that an oxygen with a positive charge 
is an electrophile (Figure 7). This error was observed in both the first step, 
addition of a proton to the alkene, and in the last step, deprotonation. For an 
expert, the hydronium is first-and-foremost a Brønsted-Lowry acid, so arrows 
would move initially to the hydrogen atom. If, however, the acidity of 
hydronium is not recognized, then this is actually a fairly logical step that 
follows the commonly used saying “electron rich attacks electron poor.”
Figure 7: The accepted mechanism of Addition Puzzle 2 in the Mechanisms app. 
Student attempts at using oxygen of hydronium as the electrophile shown below 
the corresponding steps. 
Addition Puzzle 2 also revealed that, students struggle with how to show proton 
transfers in a mechanism. Multiple times students tried to have a proton leave 
without the facilitation of a base (Figure 8a) or started the arrow from the 
hydronium oxygen-hydrogen bond (Figure 8b). Perhaps students are trying to 
complete the puzzle in the most efficient number of steps but then that means 
they are do not fully understand why the flow of electrons follows the patterns it 
does. Overall, data collected from Addition Puzzle 2 suggests students struggle 
to recognize when to use the acid-base steps traditionally learned early on in 
organic chemistry courses. Without a firm basis in this foundational chemistry 
concept, more advanced mechanisms that include an acid-base, such as those 
involving carbonyls, are going to be even more challenging for student. Ongoing 
research carried out at two midwestern research intensive universities are 
exploring these hypotheses with think-aloud studies of individual students using 
the app
.


The Mechanisms app - Development of a new learning tool for active learning 
in organic chemistry v13 COMPRESSED 
Printed 
2/28/2019 
21 
Figure 8: Common errors for deprotonation attempts a.) loss of a proton without 
the assistance of a base and b.) movement of electrons from the hydronium 
oxygen-hydrogen bond onto the base. 
As more students continue to use the app and play through the puzzles, it is 
anticipated that more unexpected common errors will be recorded. Moving 
forward, we plan to use this data to identify where students would benefit from 
more hints and explanations. The inclusion of this automated guidance through 
advanced analytics and machine learning will provide individualized feedback 
for students and concept-based assessment for instructors, and is the ultimate 
goal of the NSF SBIR research and development effort. As we continue to 
improve the content and pedagogy of Mechanisms based on user feedback, our 
hope is that we can give all students the guidance they need to succeed in 
organic chemistry. 

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